{"title":"基于非线性最小二乘法的中国天顶静压延迟精确校准模型","authors":"Kaiyun Lv, Weifeng Yang, Zhiping Chen, Pengfei Xia, Xiaoxing He, Zhigao Chen, Tieding Lu","doi":"10.1175/jtech-d-22-0111.1","DOIUrl":null,"url":null,"abstract":"\nZenith Hydrostatic Delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work firstly estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semi-annual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least square estimation method was introduced to establish an improved ZHD model-China Revised Zenith Hydrostatic Delay (CRZHD) adapted for China. The accuracy of CRZHD model was assessed using radiosonde data and IGS (International GNSS Service, IGS) data in 2017, the radiosonde data results show that CRZHD model is superior to Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS/RMSE are decreased by 2.7 /1.5 (URUM), 5.9 /5.3 (BJFS) and 9.6 /8.8 mm (TCMS). The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms Saastamoinen model (SAAS-PWV), which the precision is improved by 44.4%. The BIAS ranged from -1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0%/95.9%, while SAAS-PWV account for 46.6%/ 58.9%.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Precise Zenith Hydrostatic Delay Calibration Model in China Based on Nonlinear Least Square Method\",\"authors\":\"Kaiyun Lv, Weifeng Yang, Zhiping Chen, Pengfei Xia, Xiaoxing He, Zhigao Chen, Tieding Lu\",\"doi\":\"10.1175/jtech-d-22-0111.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nZenith Hydrostatic Delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work firstly estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semi-annual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least square estimation method was introduced to establish an improved ZHD model-China Revised Zenith Hydrostatic Delay (CRZHD) adapted for China. The accuracy of CRZHD model was assessed using radiosonde data and IGS (International GNSS Service, IGS) data in 2017, the radiosonde data results show that CRZHD model is superior to Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS/RMSE are decreased by 2.7 /1.5 (URUM), 5.9 /5.3 (BJFS) and 9.6 /8.8 mm (TCMS). The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms Saastamoinen model (SAAS-PWV), which the precision is improved by 44.4%. The BIAS ranged from -1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0%/95.9%, while SAAS-PWV account for 46.6%/ 58.9%.\",\"PeriodicalId\":15074,\"journal\":{\"name\":\"Journal of Atmospheric and Oceanic Technology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Atmospheric and Oceanic Technology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1175/jtech-d-22-0111.1\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, OCEAN\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Atmospheric and Oceanic Technology","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1175/jtech-d-22-0111.1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, OCEAN","Score":null,"Total":0}
引用次数: 1
摘要
天顶静水延迟(ZHD)是全球导航卫星系统(GNSS)导航定位和GNSS气象中的一个重要参数。由于Saastamoinen ZHD模型在中国存在较大误差,因此对Saastamoinen ZHD模型进行改进具有重要意义。本文首先利用2012 - 2016年中国73个站点的探空数据,以综合ZHD作为参考值,估算了Saastamoinen模型。然后,计算了参考值与Saastamoinen模型zhd之间的残差,并探讨了残差与气象参数之间的相关性。采用连续小波变换方法识别残差的年际和半年度特征。针对残差的非线性变化特点,引入非线性最小二乘估计方法,建立了一种适合中国国情的改进的ZHD模型——修正天顶静水时滞(CRZHD)。2017年利用探空数据和IGS (International GNSS Service, IGS)数据对CRZHD模型的精度进行了评价,结果表明,CRZHD模型优于Saastamoinen模型,提高了69.6%。具有连续气象数据的3个IGS站点的偏差/均方根误差(BIAS/RMSE)分别降低了2.7 /1.5 mm (URUM)、5.9 /5.3 mm (BJFS)和9.6 /8.8 mm (TCMS)。利用2017年的探空数据,讨论了CRZHD模型检索PWV的性能。结果表明,CRZHD模型检索PWV (CRZHD-PWV)优于Saastamoinen模型(SAAS-PWV),精度提高44.4%。CRZHD-PWV的偏差范围为-1 ~ 1 mm, RMSE范围为0 ~ 2 mm,分别占89.0%/95.9%和46.6%/ 58.9%。
A Precise Zenith Hydrostatic Delay Calibration Model in China Based on Nonlinear Least Square Method
Zenith Hydrostatic Delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work firstly estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semi-annual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least square estimation method was introduced to establish an improved ZHD model-China Revised Zenith Hydrostatic Delay (CRZHD) adapted for China. The accuracy of CRZHD model was assessed using radiosonde data and IGS (International GNSS Service, IGS) data in 2017, the radiosonde data results show that CRZHD model is superior to Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS/RMSE are decreased by 2.7 /1.5 (URUM), 5.9 /5.3 (BJFS) and 9.6 /8.8 mm (TCMS). The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms Saastamoinen model (SAAS-PWV), which the precision is improved by 44.4%. The BIAS ranged from -1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0%/95.9%, while SAAS-PWV account for 46.6%/ 58.9%.
期刊介绍:
The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.